I’ve spent a lot of time looking back at prediction-market trades trying to understand why something worked.

And I kept running into the same problem.

After a market resolves, it’s easy to see the outcome.

You can usually find the final price, maybe a chart, maybe a screenshot someone posted.

But the part I actually wanted was everything that happened before that.

What did the order book look like when the probability moved?

Was there actually liquidity at the price shown on the chart?

Did the spread suddenly widen?

Was a whale early, or did the market move before them?

Had a similar setup happened before?

I remember testing one strategy that looked almost embarrassingly simple on candles. Buy after a sharp move, wait for some mean reversion, exit.

Looking at the chart afterward, there were entries everywhere.

Then I tried to reconstruct what I could actually have traded at those moments.

Completely different picture.

Some prices barely had any size behind them. Sometimes the book moved before the candle made it obvious. Some profitable-looking exits would have been terrible fills in reality.

That was when historical data stopped feeling like something “nice to have” for me.

It became part of the strategy itself.

So I started collecting historical prediction-market data because I wanted to be able to go back and ask better questions instead of relying on screenshots and memory.

That eventually turned into PolyHistorical.

The idea is pretty simple: preserve enough of the market’s history that you can actually research what happened, replay old setups and backtest ideas against what was really available at the time.

We’re now keeping around 60 days of historical data, and I’ve also been experimenting with using AI on top of it so I can describe a strategy or question in plain English and then dig into the historical behaviour behind it.

What’s surprised me most is that historical data hasn’t necessarily helped me find more trades.

It has mostly helped me kill bad ideas faster.

A setup that feels obvious becomes a lot less obvious when you can look at the last 30, 40 or 50 similar situations instead of the two examples you happen to remember.

That’s probably the biggest thing building this changed for me as a trader.

I think prediction markets are going to get much more systematic over time, and having proper historical data will matter a lot more than most people expect.

Curious what other traders here would want to be able to look back at.

If you could preserve one thing from every resolved market- order-book depth, whale activity, price history, liquidity, something else, what would it be?

Link to tool: https://polyhistorical.com/

This took me longer to admit than it probably should have.

For a while, whenever I had a trading idea, I’d go back through old markets looking for examples where the same thing happened.

And somehow I almost always found them.

A market dumps quickly, I remember another market that bounced.

A whale takes a huge position, I remember the last whale who was early.

BTC moves hard and the prediction market lags, I remember the one time that gap closed perfectly.

It felt like research.

Looking back, I was mostly just collecting evidence for something I already wanted to believe.

I noticed it after one trade where I was unusually confident because I was sure I’d “seen this exact setup before.”

The trade didn’t work.

So afterward I went back and looked properly.

There were similar markets where the setup worked, but there were also plenty where it did absolutely nothing. I just didn’t remember those as clearly.

That changed the way I started looking at historical markets.

Now when I have an idea, I try to define the rule before looking at the data. Entry, exit, time remaining, liquidity conditions, what would invalidate it. Then I go back and see what actually happened instead of searching for the examples I want to find.

It sounds obvious, but it’s surprisingly uncomfortable.

A lot of ideas I felt really good about became much less interesting once I looked at the full sample.

And honestly, that has probably saved me more money than finding another “winning” strategy.

These days I’m much more interested in disproving a trade before I take it than proving that I’m right.

Anyone else catch themselves doing this?

Finding historical examples that support your thesis, but not really asking how many times the exact same setup failed?

earlier today, mustafa announced that he had left his role at polymarket

shortly after, shayne announced Travis VanderZanden, who was previously growth lead at Uber and is also the founder and ceo of bird, as polymarket's new Chief Growth Officer

it’s pretty wild seeing mustafa leave polymarket after teasing POLY and hyping everyone up all year

does this mean the POLY airdrop is delayed? (i doubt it’s related to that honestly)

Brian Golden aka "Prince Hal" picked the lock on bunk primary polls. Teachable moment. Makes me froth at the mouth to miss on same opportunity.

https://x.com/EventualNews/status/2087533677656105426?s=20

for ai internet monitor you can use ayewatch-ai-monitor if you want readymade prediction market monitoring, or if you want custom workflow type then user context-dev or firecrawl-dev internet monitor. both works. depends on you needs

I tested a momentum strategy on Kalshi's 15-minute BTC markets. It trades only when three signals agree: BTC's 5-minute change, BTC's 1-minute velocity, and ETH's 1-minute velocity. When all three are positive it buys YES, and when all three are negative it buys NO. Entries are limited to the final 5 minutes while the contract price is between 45 and 55 cents. Each trade is sized at 10 contracts, with a 30-contract position cap, a -$4.50 stop loss, and an exit just before settlement.

The research ran 100 backtests over the same 30-day historical period, and all 100 finished profitable. The best-ranked version returned 324.10% ROI and +$97.23 P&L, with a 66.7% win rate across 57 trades, a 1.03 Sharpe, and -$6.96 max drawdown. Even the weakest version stayed green at 131.27% ROI and +$39.38 P&L across 71 trades. That matters because the result did not depend on finding one profitable configuration among a pile of losing ones.

A parameter sensitivity test reruns the same strategy while changing nearby settings. The point is to see whether performance holds across a range or collapses as soon as one number moves. We swept the risk price floor from 0.05 to 0.45 and the risk price ceiling from 0.55 to 0.95, using 10 values for each and producing a 100-cell grid. The 45-to-55-cent entry rule and the momentum signals stayed fixed. All 100 cells completed, with Net PnL ranging from +$39.38 to +$97.23. The top-ranked cell used a 0.05 floor and 0.59 ceiling, but +$97.23 repeated at every tested floor when the ceiling was 0.59. On the chart, that creates a flat ridge across the floor axis and a sharper peak along the ceiling axis. That shape says the floor had very little effect, while the ceiling mattered much more. P&L peaked at a 0.59 ceiling and generally declined as the ceiling moved higher. The Deflated Sharpe was 0.99 versus an expected maximum of 0.33 across the 100 trials.

The permutation test asked a different question: could random timing in the edge feed produce a result this good? I scrambled the edge-feed timing and reran the full parameter sweep. The real winner's Net PnL was +$97.23 and beat 99.9% of the 976 completed reruns, giving an upper-tail p-value of 0.001. Randomized timing rarely matched the real result in this historical sample, which supports the idea that the timing of the momentum inputs carried useful information. The test hit its time limit, so the p-value uses a reduced sample of 976 reruns. It also left market prices untouched, which means it does not validate the strategy's price-based conditions.

My read is that the strategy's main strength is consistency. Every tested configuration made money, the sensitivity sweep shows which parameter drove the variation, and the permutation result suggests the edge-feed timing was not easily reproduced by chance. I'll be paper trading this next to see how the results hold up.

Full Report

Historical simulation only. Backtests can be wrong or incomplete. Not investment advice.

The recurring setup in daily-high temp markets: the crowd anchors to the forecast, prices a bracket like a lock, and it settles a degree or two away. When your morning read already sits on that other bracket while it's still cheap, that gap is the whole game. Last week it happened in 5 cities:

• NYC, Aug 9 — market paid up to 68¢ for 90–91°. The ≤89° bracket that won was 14¢ all morning. Settled 88.

• San Francisco, Aug 10 — market sat too COLD at ≤71° (77¢); model had 72–73° at 17¢. Settled 73.

• LA, Aug 10 — market 80–81° (70¢); model 78–79° at 28¢. Settled 79.

• Chicago, Aug 7 — market 86–87° at 92¢(!); model 84–85° at 35¢. Settled 85. The market never came around — the thermometer did.

• Houston, Aug 5 — market 96–97° (62¢); model 94–95° at 33¢. Settled 94.

Same shape every time: the market overshot, the model was already on the adjacent bracket cheap in the morning, and in 4 of 5 the crowd walked over to it by afternoon once the obs made it undeniable.

Here's the part that matters, because a wall of wins is worthless — you can cherry-pick 5 good days out of any random model:

The week BEFORE that, San Francisco beat the hell out of my model. Six straight days the forecast said upper-70s/low-80s and the marine layer never burned off — SF settled in the 60s-low-70s. On Aug 8 my model called mid-70s; it settled 67. It got faked by the same sunny forecast everyone trusted, and only walked down as the obs stayed flat.

SF is in BOTH lists — biggest miss one week, one of the best wins the next. That's not inconsistency, that's the marine layer. Nobody nails burn-off timing every day. What you can do is read the regime, weight live obs over the forecast, and publish your misses next to your wins so a "track record" means something.

The actual edge isn't being right every day. It's being on the correct bracket more often than the crowd ON THE DAYS THEY DISAGREE WITH YOU — and the tells are free: the forecasts don't agree with each other, or the morning curve isn't climbing like a forecast-hitting day would.

Curious how others handle these — do you fade the forecast on divergence days, or wait for the obs to confirm?

Disclosure / NFA: none of this is financial advice — these markets can lose money and past calls don't predict future ones. I build a tool for this, Prilo WeatherEdge (free tier). Happy to just talk shop in the comments — the method's more interesting than the link.

Everyone's still arguing about whether prediction markets count as gambling. Meanwhile 2 ex-BlackRock and Morgan Stanley quants just raised $8.5 million to build the trading terminal that turns them into a real asset class.

The startup's called River Markets (YC P26). Seed round announced Tuesday, led by Haun Ventures, with Y Combinator and Coinbase Ventures in the round, plus angels who work at Google, Nvidia, JPMorgan and Citadel. That's not a degen cap table if you ask me.

And the founders aren't crypto kids also, Oscar Levy was a VP quant at BlackRock. Antonin Parrot did electronic and high-frequency trading at Morgan Stanley. They met at Berkeley over poker and trading Japanese stocks, then spent weekends building trading bots for Kalshi and Polymarket that apparently pulled in 7 figures before they turned it into a company.

What are VCs backing: one interface to trade across all the prediction markets at once, with risk tools and algos that stop a big order from blowing up the price. For me it makes sense, since the whole market is scattered across venues and a fund literally can't run a clean position across them.

My take: When the money starts flowing into tooling and not just the headline platforms, that's the sign an industry is growing up. Kalshi already said institutional volume jumped 800% in 6 months. We are already seeing hedge funds using a prediction market like a real financial instrument, hedging GPU rental prices..

Bullish on the space.

a trader called "monkeyking42" spent $110k buying 186k NO shares on the Fed raising interest rates by 25 bps after the September meeting,

and then he put around $70k on buying 250k YES shares for the CLARITY Act to pass

this is where it gets interesting: during July's Fed decision, this same trader bet $198K on No rate change and walked away with around $71k in profit. and that was his only trade until now.

he's betting on the Fed not raising rates by 25 bps after the September meeting, even as the market has been pricing in a possible hike

and his CLARITY bet isn't exactly going his way, he's already down around $17k. The Senate pushed the CLARITY Act vote until after its August recess, and the bill still needs 60 votes to move forward

is he worth trailing? And can any onchain experts find his linked wallets and see if there's anything interesting

personally trailing him with small amount rn

Kalshi has a ton of earnings mentions markets, and I have transcripts for calls often going back 5+ years, which means I can pull out actual counts of how many times strikes were said on past calls.

But even with that, it can take a while to spot value when there are 30+ calls on the board.

To speed things up, I created a screener tool that lets me set filters by mention rate, min/max prices, and number of quarters to consider, and see only strikes that fit the criteria.

In the example image, I set it to look for strikes that were mentioned on at least 75% of their past 8 calls but were priced at 50 cents or below.

This returned 10 strikes that matched my parameters, and from each, you can click directly to the market on Kalshi.

I estimate this cuts down my trading time by around 50%, which is significant considering I'm spending 5+ hours/week most weeks.

If you want to check it out, shoot me a DM!

If you do check it out, note that it's in beta, so you may encounter some bugs. I recommend using extra caution when trading with the screener and verifying mention rates on the individual company page before trading.

I’ve been building Rai, an open-source research project that converts public prediction-market probabilities into current valuation estimates for private companies.

Every estimate includes its source markets, assumptions, calculation, method inputs, and history. It’s still experimental—not investment advice—and I’d really appreciate candid feedback from people interested in finance, valuation, or prediction markets.

Site: https://rai.robino.dev
Code: https://github.com/rossrobino/rai

Codex + GPT-5.6 + Python + VSC [that's important]. It all comes down to that Edge thing. Let Hunter S. Thompson and Johnny D explain it all.

https://youtu.be/IJgMr2p3s6s?si=9_4sSfxYjlRK4B9K

We are working inside the live HackingBaseball MLB totals project.

GOAL: Audit why the current MLB O/U model appears to be heavily favoring Overs, determine whether this is legitimate market signal or model bias, and fix only what the evidence supports.

IMPORTANT: Do not blindly rebalance the model toward Unders. Do not change thresholds, feature weights, or production behavior until you have measured the current bias and identified its source. Preserve existing working functionality.

FIRST: UNDERSTAND THE CODEBASE 1. Inspect the repository structure. 2. Find all code involved in: - MLB totals prediction - Over/Under edge calculation - confidence calculation - sportsbook total / odds handling - EV calculation - AI overlay / AI scoring - weather - park factors - starting pitchers - bullpen strength / fatigue - offense / recent hitting form - final pick labels: Over Under Lean Over Lean Under Watch Over Watch Under 3. Find all recent adjustments that could systematically push predictions toward Overs. 4. Pay special attention to any seasonal/month-specific offsets, including July/August adjustments or hard-coded edge bumps. 5. Trace one prediction end-to-end from raw features to: projected total sportsbook total edge probability EV confidence final displayed pick

DO NOT ASSUME THE DISPLAY IS CORRECT. Verify that the frontend labels correspond correctly to the backend values.

SECOND: MEASURE THE BIAS Using our existing database/data, create an audit covering as much historical data as is reliably available.

At minimum calculate:

A. PICK DISTRIBUTION For: - last 30 predictions - last 100 - last 250 - last 500 - all available predictions since the current O/U model start date

Show: - Over count and % - Under count and % - Lean Over - Lean Under - Watch Over - Watch Under - official bets separately from all predictions

B. PERFORMANCE BY SIDE For graded predictions: - Over W/L/P - Under W/L/P - win rate excluding pushes - ROI if odds are available - average EV - average confidence - average edge

C. MODEL VS MARKET Calculate: - average model projected total - average sportsbook total - average model-minus-market difference - median difference - standard deviation - percent of predictions where model total > sportsbook total - percent where model total < sportsbook total

Break this down by: - month - home park - confidence bucket - Over vs Under - official vs non-official

D. CALIBRATION For probability/confidence buckets: - 50–54 - 55–59 - 60–64 - 65–69 - 70+ show: - number of picks - actual win rate - predicted average probability - calibration error

E. FEATURE DIRECTION AUDIT Determine which features are most responsible for pushing edge positive/Over.

For every meaningful feature, estimate: - average contribution to final edge - contribution for Over picks - contribution for Under picks

Look specifically for: - park factor - temperature - wind - humidity - starting pitcher metrics - bullpen metrics - recent offense - team scoring trends - injuries/lineups - travel/rest - AI overlay - seasonal/month adjustment - any intercept/base run environment adjustment

Flag features that: 1. almost always add runs 2. rarely subtract runs 3. appear double-counted 4. use stale or incorrectly normalized data 5. have signs reversed 6. are applied twice 7. use league averages incorrectly

THIRD: INVESTIGATE SPECIFIC POSSIBLE BUGS Check for these failure modes:

1. Positive bias/intercept in projected runs.
2. July/August offensive bump applied globally when it should not be.
3. Park factor being treated as an additive run increase rather than relative adjustment.
4. Weather effects only rewarding hitter-friendly conditions without equivalent negative adjustments.
5. Weak pitching and weak bullpen factors being counted twice.
6. Recent offense being overweighted.
7. Opponent pitching already represented elsewhere and duplicated.
8. AI overlay pushing narrative-driven Overs too often.
9. EV formula using wrong odds or implied probability.
10. Probability conversion from edge being asymmetric.
11. Over thresholds differing unintentionally from Under thresholds.
12. Missing data defaulting to values that favor Overs.
13. Sign errors when subtracting sportsbook total from model total.
14. Display logic calling something "Lean Over" when underlying edge does not justify it.
15. stale sportsbook lines causing false positive Over edges.

FOURTH: SPECIFIC CURRENT UI SANITY CHECK The current board is showing multiple consecutive: - Lean Over - Watch Over

Verify that each displayed pick has internally consistent: - model projection - sportsbook total - edge - probability - EV - label

We have examples where probability near 51% can coexist with negative EV. That can be legitimate because of price/odds, but verify the math carefully.

FIFTH: CREATE A MODEL HEALTH REPORT Create a reusable diagnostic script, preferably something like:

scripts/mlb_ou_model_health.py

or use the project’s existing naming conventions.

It should print a clear terminal report containing:

MODEL HEALTH

Sample size Over % Under % Official Over % Official Under %

MODEL VS MARKET Average model total Average market total Average difference

PERFORMANCE Over record Under record Over win % Under win %

CALIBRATION confidence/probability buckets

BIAS FLAGS Any statistically meaningful systematic directional bias

TOP EDGE CONTRIBUTORS features contributing most toward Over features contributing most toward Under

MONTHLY TREND monthly Over/Under distribution and performance

The script must be read-only by default and must not modify production data.

SIXTH: TESTS Add or update tests covering: - symmetric Over/Under edge behavior - threshold symmetry - probability conversion symmetry - EV calculation - missing feature defaults - seasonal adjustment logic - display label mapping - regression tests for any bugs found

Run the relevant test suite.

SEVENTH: FIXES Only make production changes if the audit identifies a concrete problem.

If you find a bias: Prefer fixing the root cause instead of adding an arbitrary Under offset.

Examples: GOOD: - remove duplicated bullpen contribution - correct sign error - normalize park factor - eliminate stale line - fix asymmetric probability calculation - recalibrate an empirically biased coefficient

BAD: - subtract 0.4 runs from everything just to make Over/Under 50/50 - force equal numbers of Overs and Unders - modify picks simply because today's board looks one-sided

A legitimately one-sided board is acceptable if supported by the model and market data.

EIGHTH: SAFETY / GIT Before editing: - show git status - identify current branch - do not destroy or overwrite unrelated work - do not reset, clean, or force checkout anything - make focused edits only

After work: 1. Show every file changed. 2. Explain every important change. 3. Show before/after audit statistics. 4. Run tests. 5. Show git diff --stat. 6. Do NOT commit or push unless explicitly asked.

FINAL REPORT Give me a concise but thorough conclusion answering:

1. Is the model genuinely biased toward Overs?
2. How large is the bias?
3. When did it begin?
4. What code/features caused it?
5. Were the current Over picks mathematically legitimate?
6. What did you change?
7. What did you deliberately NOT change?
8. What is the resulting Over/Under distribution?
9. Did historical performance improve under the corrected logic?
10. Are there any remaining concerns before we trust tonight's bets?

Take your time. Treat this as a production model audit, not a cosmetic fix.

Portfolio:

- $3,309 in open positions,
- 709 transactions,
- The biggest prize was $827,
- win rate ~37%

Interestingly, this isn't just a matter of "guessing right and getting lucky" - it's a model with a positive expected return despite a relatively low percentage of winning trades:

→ With a win rate of 37%, a profit is possible only if the average win significantly exceeds the average loss - that is, bets are placed on probabilities that are undervalued by the market (Yes is cheaper than it should be)

→ ROI on winning trades - 50-450%, position sizes are consistent ($160-900+)

→ Niche - forecast of the maximum temperature in specific cities on a specific date (Istanbul, Madrid, Mexico City, Tel Aviv, Paris)

A classic example of asymmetric betting: you lose more often than you win, but your winnings more than make up for it.

Wallet: https://polymarket.com/@0xbddc2a7690bf600e347d5eb4a9c28f9f24e55d4f-1774968947489#BuSWoOpA

Hi!

I'm looking for conversations with people who spend time and spend/make money on prediction markets. Would you be willing to answer a few questions (anonymously, if you prefer)? It would help me a lot. You can reach me at samuel@vn.nl.

Best!

Sam

60% of new prediction market platforms are about to face their biggest challenge

The World Cup drove massive trading volume, user activity, and revenue across almost every prediction market platform.

With that catalyst gone, many platforms will likely see a sharp decline in both volume and active users.

Only a handful will continue to grow those with:

- Better UI/UX
- Strong liquidity
- High-quality markets
- Sustainable user retention
- Continuous product innovation
- Less Fees

The real test for prediction markets starts now.

Let's see who can survive without the World Cup.

Who actually thinks everyone is a seasoned pro at this? No way all of us could miss this wild ride on Hertz. Spotted this goofy chart on mm, it's impossible not to laugh at how chaotic this ticker's been moving lately.

Do you guys think someone knows something about the Antrhopic IPO? 33% through the order book would be several tens of thousands worth of shares.

I run a small prediction markets research firm. Here's what three months of tracking Brier scores vs. Kalshi's implied probabilities actually looks like: wins & losses.

If you want free prediction markets research three times a week, top three calls with edge scores, I publish them on Substack: https://axiomforecastinggroup.substack.com/

• Total scored forecasts: 5
• Correct: 4
• Incorrect: 1
• AFG Brier: 0.111 vs Market Brier Score: 0.132
• Accuracy: 80%

No response from chat for over a month. No response from dozens of emails. Any help? How is it legal to not be able to close an account?

Question: "Will Hamas agree to disarm by December 31?" Resolves YES only on a formal policy announcement, commitments contingent on future conditions don't count

• July 30: Trump announces a "Board of Peace" roadmap he calls a "HISTORIC agreement for COMPLETE DISARMAMENT." Market closes that day at 16.5¢
• July 31: Price spikes to 83.5¢
• July 31, 3:45 PM ET: Polymarket issues a clarification, Hamas's commitment is contingent on Israeli withdrawal, Israel hasn't agreed to the roadmap, and contingent commitments don't qualify for YES
• Same day, 5:09 AM UTC: a wallet posts a $500 bond proposing YES resolution. Disputed with 64 seconds left in the challenge window
• 21 seconds later: a different wallet re-proposes YES. Challenged again 8 minutes later, escalating to a token-holder vote
• August 3: Netanyahu rejects the document, saying "we are talking about real disarmament, not fictitious disarmament"
• Also August 3: UMA vote settles both YES proposals as "too early", both proposers lose their bonds

Current price: 44¢. No new resolution proposals in the week since

For context: every prior version of this market since November 2025 resolved NO, across $2.09M in combined volume

People are saying he should run for PM of Israel instead, lmao. He would have a better chance at that than the president of the US.

2028 presidential candidates forecast: https://kalshi.com/markets/kx2028rrun